ELDP: Extended Link Duration Prediction Model for Vehicular Networks. (11th April 2016)

• Record Type: Journal Article
• Authors: Wang, Xiufeng; Wang, Chunmeng; Cui, Gang; Yang, Qing; Zhang, Xuehai
• Other Names: Bellavista Paolo Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2016)
• Publication Date: 2016-04-11
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2016/5767569
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : Link duration between two vehicles is considered an important quality of service metric in designing a network protocol for vehicular networks. There exist many works that study the probability density functions of link duration in a vehicular network given various vehicle mobility models, for example, the random waypoint model. None of them, however, provides a practical solution to estimating the link duration between two vehicles on the road. This is in part because link duration between vehicles is affected by many factors including the distance between vehicles, their turning directions at intersections, and the impact of traffic lights. Considering these factors, we propose the extended link duration prediction (ELDP) model which allows a vehicle to accurately estimate how long it will be connected to another vehicle. The ELDP model does not assume that vehicles follow certain mobility models; instead, it assumes that a vehicle's velocity follows the Normal distribution. We validate the ELDP model in both highway and city scenarios in simulations. Our detailed simulations illustrate that relative speed between vehicles plays a vital role in accurately predicting link duration in a vehicular network. On the other hand, we find that the turning directions of a vehicle at intersections have subtle impact on the prediction results.